How AI accelerates China’s drug discovery process

Insilico Medicine has cut the timeline for generating drug development candidates to roughly one year by integrating artificial intelligence with laboratory research in China, CEO Alex Zhavoronkov stated.

According to Zhavoronkov, the Hong Kong-listed firm’s most advanced program achieved candidate nomination in just nine months, with a typical duration of 13 months. This contrasts sharply with conventional methods, which usually require about four-and-a-half years to reach the same milestone.

These timelines refer specifically to early discovery and candidate selection, not the complete drug commercialization process. Clinical trials, manufacturing, and regulatory approvals remain distinct, subsequent phases.

AI accelerates candidate selection

Insilico employs generative AI to pinpoint biological targets, design potential drug molecules, and evaluate which compounds warrant progression to laboratory testing.

The company reports that its programs typically reach preclinical-candidate nomination within 12 to 18 months after researchers synthesize and test between 60 and 200 molecules. This workflow merges AI-generated designs with human review and experimental validation.

Laboratory experiments are still essential to verify the biological activity and drug properties of compounds identified by the models. Insilico notes that its AI-supported approach enables teams to reach candidate nomination after testing fewer synthesized molecules, though it has not provided a direct comparison with equivalent non-AI programs.

Since 2021, Insilico has generated 31 preclinical candidates. Thirteen programs have received investigational new drug clearances, permitting advancement toward human studies, according to the company’s pipeline disclosures.

The company conducts AI research in Montreal and Abu Dhabi, while extensive experimental validation and laboratory scale-up occur in China. Its Shanghai facility has automated aspects of biological sampling and compound screening.

Teams outside China develop and evaluate the company’s AI models, whereas researchers in Shanghai manage biological testing, screening, and scale-up.

Zhavoronkov attributes part of the accelerated development cycle to China’s research infrastructure, lower operating costs, and regulatory environment. He stated that pharmaceutical companies maintaining research laboratories in China can reduce traditional candidate-development timelines by approximately two years.

China has evolved beyond manufacturing generic drug ingredients to play a significant role in developing new medicines. International drugmakers increasingly collaborate with Chinese laboratories, contract research organizations, clinical-trial centers, and biotechnology firms.

A Pfizer executive noted that clinical development in China could proceed three times faster and at roughly half the cost of equivalent work in Europe. Drug candidates typically take five to seven years to reach the Chinese market, compared with at least eight to 10 years in Western markets, according to Reuters.

In 2025, China introduced a 30-working-day review pathway for eligible Class I innovative-drug clinical-trial applications. Applications requiring expert consultation or involving complex technical issues may be extended to a 60-working-day review period.

“We now compete with Chinese pharmaceutical companies on timelines, and with traditional biotechnology companies in the West on novelty,” Zhavoronkov said.

Insilico has entered research and development agreements with pharmaceutical companies including Eli Lilly and Japan’s Takeda.

The company and Taiwan-based Bora Pharmaceuticals also announced a proposed strategic alliance that could exceed $2.5 billion if definitive agreements are signed and the collaboration is fully implemented.

Although Insilico operates research facilities in China, Zhavoronkov stated that more than 90% of its revenue originates from Western pharmaceutical companies. He did not disclose the specific revenue generated within China.

Western licensing agreements are more lucrative for Insilico because China’s national insurance system offers lower reimbursement rates for highly novel drugs, Zhavoronkov explained.

The company also restricts sales of most of its software within China due to geopolitical concerns, Zhavoronkov said. It plans to expand its research operations in Shanghai.

Rentosertib advances toward Phase III trials

Insilico announced and registered a Phase III trial of Rentosertib in July 2026. This oral drug is being studied for idiopathic pulmonary fibrosis, a condition causing progressive lung scarring.

The company utilized AI to identify the drug’s biological target and generate and optimize its molecular structure.

The Phase III study aims to enroll 320 participants across 47 centers in China. It will compare Rentosertib with a placebo over 52 weeks, with the primary endpoint measuring the annual rate of decline in forced vital capacity, a standard indicator of lung function.

The trial was listed as not yet recruiting when its ClinicalTrials.gov record was updated on July 7. Enrollment was expected to begin in August 2026, with primary completion estimated for October 2029.

Rentosertib previously completed a smaller Phase IIa study. The Phase III trial will evaluate the treatment in a larger patient group over an extended period.

Candidate nomination remains an early development milestone. Drugs must still complete preclinical testing, human trials, manufacturing validation, and regulatory review before receiving approval for sale.

Industry data have not yet established whether AI-designed drugs are more likely to succeed in later-stage trials.

A 2024 analysis of AI-native biotechnology pipelines reported Phase I success rates between 80% and 90%. The same study found a Phase II success rate of approximately 40%, broadly aligning with historical industry benchmarks used by the researchers.

The researchers noted that the number of Phase II programs was too small to determine whether AI improves later-stage clinical success. The analysis was based on publicly reported pipelines and did not compare otherwise identical AI-supported and conventional drug programs.

Insilico stated it has produced 31 preclinical candidates and secured 13 investigational new drug clearances. Rentosertib is its first program to reach the Phase III stage, while none of the company’s experimental medicines has received commercial approval.

Automation reshapes biotech roles

AI and laboratory robotics are also transforming staffing requirements within Insilico.

Zhavoronkov estimated that the company could automate or displace about 40% of its software-side workforce. He clarified that this figure does not represent an announced staff reduction nor apply to the broader biotechnology industry.

Insilico employs approximately 400 people. Laboratory scientists and software engineers are being retrained to manage AI evaluation systems, automated equipment, and robotics, Zhavoronkov said.

This retraining focuses on AI benchmarks and robotic systems as the company automates more research and software functions, he added.

See also: Bristol Myers Squibb acquires Nvidia AI system for drug discovery

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